SparseCCL: Connected Components Labeling and Analysis for sparse images

Hennequin, A. M.; Couturier, B.; Gligorov, Vladimir; Lacassagne, Lionel

doi:10.1109/dasip48288.2019.9049184

Cited by 5 publications

(5 citation statements)

References 16 publications

(28 reference statements)

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“…In this paper, the segmentation and ground plane line extraction algorithm proposed in [11] is applied to remove the ground points, and residual obstacle points are clustered into many clusters with a seed-filling connected components labeling algorithm [44]. Moreover, we search a wide range of the following values and select those that yield the best estimation.…”

Section: Implementation Details and Compared Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

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Section: Implementation Details and Compared Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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“…However, this greater simplicity comes with a "frame rate" requirement (30 MHz) that is orders of magnitude larger than typical image processing rates (<1 kHz). In fact, a CPU implementation exists of the same VELO clustering task discussed in this that was inspired by some algorithms in use in image processing problems, appropriately revisited to exploit the smallness of the size of the components and their sparseness [20].…”

Section: Further Considerationsmentioning

confidence: 99%

A FPGA-Based Architecture for Real-Time Cluster Finding in the LHCb Silicon Pixel Detector

Bassi

Giambastiani

Hennessy

et al. 2023

IEEE Trans. Nucl. Sci.

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This article describes a custom [very high speed integrated circuits (VHSIC) hardware description language (VHDL)] firmware implementation of a 2-D cluster-finder architecture for reconstructing hit positions in the new vertex pixel detector that is part of the large hadron collider beauty (LHCb) Upgrade. This firmware has been deployed to the existing field-programmable gate-array (FPGA) cards that perform the readout of the VErtex LOcator detector (VELO), as a further enhancement of the data-acquisition (DAQ) system, and will run in real-time during physics data taking, reconstructing VELO hits coordinates on-the-fly at the LHC collision rate. This preprocessing allows the first level of the software trigger to accept an 11% higher rate of events, as the ready-made hit coordinates accelerate the track reconstruction and consume significantly less electrical power. It additionally allows the raw pixel data to be dropped at the readout level, thus saving approximately 14% of the DAQ bandwidth. Detailed simulation studies have shown that the use of this real-time cluster-finding does not introduce any appreciable degradation in the tracking performance in comparison to a full-fledged software implementation. This work is part of a wider effort aimed at boosting the real-time processing capability of HEP experiments by delegating intensive tasks to dedicated computing accelerators deployed at the earliest stages of the data acquisition chain.

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“…However, this greater simplicity comes with a 'frame rate' requirement (30 MHz) that is orders of magnitude larger than typical image processing rates (<1 kHz). In fact, a CPU implementation exists of the same VELO clustering task discussed in this paper that was inspired by some algorithms in use in image processing problems, appropriately revisited to exploit the smallness of the size of the components and their sparseness [20].…”

Section: Fpga Resource Usage and Throughputmentioning

confidence: 99%

A FPGA-based architecture for real-time cluster finding in the LHCb silicon pixel detector

Bassi¹,

Giambastiani²,

Hennessy³

et al. 2023

Preprint

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This article describes a custom VHDL firmware implementation of a two-dimensional cluster-finder architecture for reconstructing hit positions in the new vertex pixel detector (VELO) that is part of the LHCb Upgrade. This firmware has been deployed to the existing FPGA cards that perform the readout of the VELO, as a further enhancement of the DAQ system, and will run in real time during physics data taking, reconstructing VELO hits coordinates on-the-fly at the LHC collision rate. This pre-processing allows the first level of the software trigger to accept a 11% higher rate of events, as the ready-made hits coordinates accelerate the track reconstruction and consumes significantly less electrical power. It additionally allows the raw pixel data to be dropped at the readout level, thus saving approximately 14% of the DAQ bandwidth. Detailed simulation studies have shown that the use of this real-time cluster finding does not introduce any appreciable degradation in the tracking performance in comparison to a full-fledged software implementation. This work is part of a wider effort aimed at boosting the real-time processing capability of HEP experiments by delegating intensive tasks to dedicated computing accelerators deployed at the earliest stages of the data acquisition chain.

show abstract

SparseCCL: Connected Components Labeling and Analysis for sparse images

Cited by 5 publications

References 16 publications

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

A FPGA-Based Architecture for Real-Time Cluster Finding in the LHCb Silicon Pixel Detector

A FPGA-based architecture for real-time cluster finding in the LHCb silicon pixel detector

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